TY - GEN
T1 - Stabilizing the explicit euler integration of stiff and undamped linear systems
AU - Gurfil, Pini
AU - Klein, Itzik
PY - 2007
Y1 - 2007
N2 - Euler's integration methods are frequently used for numerical integration as well as real-time implementation of linear systems. However, when the integrated system is either undamped or stiff, Euler's explicit integration becomes unstable regardless of the forcing input. In this work, it is shown that this instability can be avoided by a judicious selection of the state variables. Instead of the generalized coordinates and velocities, it is proposed to define state variables based on the method of variation-of-parameters (VOP). It is proven that the VOP-based state variables yield a bounded numerical error for undamped as well as stiff systems provided that the forcing inputs are bounded. The analysis is performed for both deterministic and stochastic inputs. In the stochastic case, the numerically-calculated covariance matrix entries diverge when using the generalized coordinates and velocities but remain bounded when implementing the VOP-based approach. The newly developed formalism is illustrated by a number of examples of practical interest, showing that the VOP-based approach is also more computationally efficient than the standard approach.
AB - Euler's integration methods are frequently used for numerical integration as well as real-time implementation of linear systems. However, when the integrated system is either undamped or stiff, Euler's explicit integration becomes unstable regardless of the forcing input. In this work, it is shown that this instability can be avoided by a judicious selection of the state variables. Instead of the generalized coordinates and velocities, it is proposed to define state variables based on the method of variation-of-parameters (VOP). It is proven that the VOP-based state variables yield a bounded numerical error for undamped as well as stiff systems provided that the forcing inputs are bounded. The analysis is performed for both deterministic and stochastic inputs. In the stochastic case, the numerically-calculated covariance matrix entries diverge when using the generalized coordinates and velocities but remain bounded when implementing the VOP-based approach. The newly developed formalism is illustrated by a number of examples of practical interest, showing that the VOP-based approach is also more computationally efficient than the standard approach.
UR - http://www.scopus.com/inward/record.url?scp=37249019037&partnerID=8YFLogxK
U2 - 10.2514/6.2007-6446
DO - 10.2514/6.2007-6446
M3 - Conference contribution
AN - SCOPUS:37249019037
SN - 1563479044
SN - 9781563479045
T3 - Collection of Technical Papers - AIAA Guidance, Navigation, and Control Conference 2007
SP - 1446
EP - 1463
BT - Collection of Technical Papers - AIAA Guidance, Navigation, and Control Conference 2007
PB - American Institute of Aeronautics and Astronautics Inc.
T2 - AIAA Guidance, Navigation, and Control Conference 2007
Y2 - 20 August 2007 through 23 August 2007
ER -